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Learner Reviews & Feedback for Advanced Learning Algorithms by DeepLearning.AI

4.9
stars
6,979 ratings

About the Course

In the second course of the Machine Learning Specialization, you will: • Build and train a neural network with TensorFlow to perform multi-class classification • Apply best practices for machine learning development so that your models generalize to data and tasks in the real world • Build and use decision trees and tree ensemble methods, including random forests and boosted trees The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key theoretical concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start....

Top reviews

DG

Apr 14, 2023

Extremely educational with great examples. Helpful to know Python beforehand or the syntax will become a time sync, and understanding the mathematics as going through the class makes it a decent pace.

SL

Aug 27, 2022

After copleting the course I found all conceptual knowlegde for visualising and implementing the algorithm in my work. Before this course I was not using the full potential of the advanced algorithm

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951 - 975 of 1,068 Reviews for Advanced Learning Algorithms

By Muhammad A X

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Oct 15, 2024

cool

By mahdi t

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Sep 27, 2024

best

By Priyanshu K

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Sep 11, 2024

good

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Aug 20, 2024

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Aug 4, 2024

nice

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Jul 29, 2024

BEST

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Jul 28, 2024

Good

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Nov 16, 2023

Good

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Nov 11, 2023

nice

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Oct 14, 2023

good

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Oct 12, 2023

good

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Oct 8, 2023

Joss

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Sep 19, 2023

good

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Apr 5, 2023

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Mar 23, 2023

cool

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Feb 17, 2023

Good

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Feb 4, 2023

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By Marlon S V L

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Jan 15, 2023

Good

By Arkadiusz J

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Mar 5, 2024

:)

By Jaber

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Sep 3, 2022

<3

By Shreyas R

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Dec 27, 2024

W

By Bhavesh P

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Jul 9, 2023

By Serge B

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Nov 30, 2022

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By Will S

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Jan 3, 2023

Really good conceptual teaching of ANNs and decision trees, but it's a little lacking in the Python implementation. It teaches you how to program an ANN with any number of layers/neurons, but there is no mention of finding the "optimal" number of each. The last week on decision trees and ensemble models feels rushed as there is only one lab and required assignment, so it completely misses the Python implementation of XGBoost. However, it teaches the essential functions in each library, so one can easily continue his or her learning with Kaggle competitions and Stack Overflow. In the end, it's meant to introduce working professionals to the most common ML models in the world today, and it does that very well, but not much more.